Dynamic Economic Emission Dispatch Considering Wind Uncertainty Using Non-Dominated Sorting Crisscross Optimization

Cl Chinnadurrai,T Aruldoss Albert Victoire

doi:10.1109/access.2020.2995213

Cl Chinnadurrai, T Aruldoss Albert Victoire

Open Access

https://doi.org/10.1109/access.2020.2995213

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Abstract

This paper presents a multi objective crisscross optimization to solve dynamic economic emission dispatch with wind-power uncertainty. The dynamic economic dispatch with combined emission requirements is formulated as a multi-objective optimization problem. The wind power output is predicted as an uncertain model and varies within a bounded limit. Minimizing the wind curtailment is added as an objective to the existing problem objectives of minimizing cost and emissions. Multi-objective crisscross optimization is proposed to solve the problem, utilizing a fast non-dominated sorting principle to obtain the optimal Pareto set of solutions. The proposed non-dominated sorting also ensures diversity, elitism and various complexities due to the high dimensionality of the problem. Exploration for global convergence and exploitation for a better solution is governed by two operators, namely, horizontal crossover and vertical crossover. The proposed solution technique is applied to standard multi-objective benchmark test problems and subsequently to standard dynamic economic dispatch problems with different ratios of wind power penetration.

Highlights

With rapid developments in integrating renewable energy power generation with existing power system networks, complexities proliferate
By integrating renewable energy resources, the electricity industry can be regulated according to the Clean Air Act amendments [2], reducing the level of emissions dispersed in the atmosphere
SIMULATION RESULTS AND DISCUSSION the solution technique is initially investigated for multi-objective benchmark test functions, and the optimal Pareto front is obtained

Summary

INTRODUCTION

With rapid developments in integrating renewable energy power generation with existing power system networks, complexities proliferate. A scenario-based stochastic programming framework is presented to solve the multi-objective economic emission dispatch problem by integrating wind power in [30]–[32]. Algorithms such as self-adaptive particle swarm optimization [31] and learning automata optimization [32] are implemented with improvements to their learning strategy.

PROBLEM FORMULATION

MINIMIZATION OF FUEL COST

MINIMIZATION OF EMISSION RATE

NON- DOMINATED SORTING CRISSCROSS OPTIMIZATION ALGORITHM

SIMULATION RESULTS AND DISCUSSION

CONCLUSION